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Which kernel function is commonly used in SVM to handle non-linear data?

Practice Questions

Q1
Which kernel function is commonly used in SVM to handle non-linear data?
  1. Linear kernel
  2. Polynomial kernel
  3. Radial basis function (RBF) kernel
  4. Sigmoid kernel

Questions & Step-by-Step Solutions

Which kernel function is commonly used in SVM to handle non-linear data?
  • Step 1: Understand that SVM (Support Vector Machine) is a type of machine learning algorithm used for classification tasks.
  • Step 2: Know that sometimes data is not linearly separable, meaning you can't draw a straight line to separate different classes.
  • Step 3: To handle non-linear data, we need a way to transform it into a space where it can be separated by a straight line.
  • Step 4: The Radial Basis Function (RBF) kernel is a mathematical function that helps in this transformation.
  • Step 5: The RBF kernel takes the original data and maps it into a higher-dimensional space, making it easier to separate the classes.
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